Systems and methods for numerically evaluating vasculature

ABSTRACT

Systems and methods are disclosed for providing a cardiovascular score for a patient. A method includes receiving, using at least one computer system, patient-specific data regarding a geometry of multiple coronary arteries of the patient; and creating, using at least one computer system, a three-dimensional model representing at least portions of the multiple coronary arteries based on the patient-specific data. The method also includes evaluating, using at least one computer system, multiple characteristics of at least some of the coronary arteries represented by the model; and generating, using at least one computer system, the cardiovascular score based on the evaluation of the multiple characteristics. Another method includes generating the cardiovascular score based on evaluated multiple characteristics for portions of the coronary arteries having fractional flow reserve values of at least a predetermined threshold value.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/656,183, filed on Oct. 19, 2012, the entirety of which is herebyincorporated herein by reference.

TECHNICAL FIELD

Embodiments of the present disclosure include systems and methods fornumerically evaluating vasculature, such as scoring coronary vasculatureby modeling coronary anatomy. More particularly, embodiments aredirected to systems and methods for patient-specific scoring of coronaryvasculature based on coronary anatomy and optionally also on fractionalflow reserve or other functional metrics values calculated fromcomputerized modeling of blood flow.

BACKGROUND

Coronary artery disease may produce coronary lesions in the bloodvessels providing blood to the heart, such as a stenosis (abnormalnarrowing of a blood vessel). As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as chronic stable angina duringphysical exertion or unstable angina when the patient is at rest. A moresevere manifestation of disease may lead to myocardial infarction, orheart attack.

A need exists to provide more accurate data relating to coronarylesions, e.g., size, shape, location, functional significance (e.g.,whether the lesion impacts blood flow), etc. Patients suffering fromchest pain and/or exhibiting symptoms of coronary artery disease may besubjected to one or more tests that may provide some indirect evidencerelating to coronary lesions. For example, noninvasive tests may includeelectrocardiograms, biomarker evaluation from blood tests, treadmilltests, echocardiography, single positron emission computed tomography(SPECT), and positron emission tomography (PET). These noninvasivetests, however, typically do not provide a direct assessment of coronarylesions or assess blood flow rates. The noninvasive tests may provideindirect evidence of coronary lesions by looking for changes inelectrical activity of the heart (e.g., using electrocardiography(ECG)), motion of the myocardium (e.g., using stress echocardiography),perfusion of the myocardium (e.g., using PET or SPECT), or metabolicchanges (e.g., using biomarkers).

For example, anatomic data may be obtained noninvasively using coronarycomputed tomographic angiography (CCTA). CCTA may be used for imaging ofpatients with chest pain and involves using computed tomography (CT)technology to image the heart and the coronary arteries following anintravenous infusion of a contrast agent. However, CCTA also cannotprovide direct information on the functional significance of coronarylesions, e.g., whether the lesions affect blood flow. In addition, sinceCCTA is purely a diagnostic test, it cannot be used to predict changesin coronary blood flow, pressure, or myocardial perfusion under otherphysiologic states, e.g., exercise, nor can it be used to predictoutcomes of interventions.

Thus, patients may also require an invasive test, such as diagnosticcardiac catheterization, to visualize coronary lesions. Diagnosticcardiac catheterization may include performing conventional coronaryangiography (CCA) to gather anatomic data on coronary lesions byproviding a doctor with an image of the size and shape of the arteries.CCA, however, does not provide data for assessing the functionalsignificance of coronary lesions. For example, a doctor may not be ableto diagnose whether a coronary lesion is harmful without determiningwhether the lesion is functionally significant. Thus, CCA has led towhat has been referred to as an “oculostenotic reflex” of someinterventional cardiologists to insert a stent for every lesion foundwith CCA regardless of whether the lesion is functionally significant.As a result, CCA may lead to unnecessary operations on the patient,which may pose added risks to patients and may result in unnecessaryheath care costs for patients.

During diagnostic cardiac catheterization, the functional significanceof a coronary lesion may be assessed invasively by measuring thefractional flow reserve (FFR) of an observed lesion. FFR is defined asthe ratio of the mean blood pressure downstream of a lesion divided bythe mean blood pressure upstream from the lesion, e.g., the aorticpressure, under conditions of maximally increased coronary blood flow,e.g., induced by intravenous administration of adenosine. The bloodpressures may be measured by inserting a pressure wire into the patient.Thus, the decision to treat a lesion based on the determined FFR may bemade after the initial cost and risk of diagnostic cardiaccatheterization has already been incurred.

Another technique for evaluating a patient's coronary vasculature is theSYNTAX scoring system and method, which is a technique to score thecomplexity and severity of coronary artery disease. The SYNTAX score isa rating method used to help determine whether patients should betreated with percutaneous coronary intervention (PCI) or coronary arterybypass graft (CABG). Standard SYNTAX scoring is performed by evaluatinga patient's coronary anatomy via angiograms, answering a series ofquestions, and assigning point values based on the answers to thequestions. For example, a cardiologist may review a patient's angiogramand assign penalty points for lesions based on, for example, eachlesion's type, shape, and location in the patient's coronary tree. Theassessed points are then added together to output a single SYNTAX scorefor the patient. In one exemplary embodiment, if the score is less than34, PCI is appropriate, and if the score is greater than 34, then CABGwill more likely produce a better outcome. It has been found that SYNTAXscoring is very effective in evaluating the complexity and extent ofdisease in a patient's coronary vasculature, and identifying the mostappropriate intervention (e.g., PCI vs. CABG). An embodiment of theSYNTAX scoring system is operating at http://www.syntaxscore.com/, whichincludes a scoring tutorial, vessel segment definitions, and scoringcalculator, the entire disclosure of which is incorporated herein byreference.

Despite its significant benefits, SYNTAX scoring is traditionally aninvasive technique because it is performed based on angiograms.Moreover, SYNTAX scoring can be a very time consuming process, as itrequires cardiologists to manually/visually evaluate angiograms, answera long series of questions, and assign a score based on each answer. Inaddition, the results of SYNTAX scoring are cardiologist-dependent, andmay vary somewhat based on how one cardiologist perceives and/or scoresangiogram images compared to how other cardiologists do so. For thisreason, SYNTAX scoring is sometimes performed by several cardiologists,e.g. a three-person panel, so as to generate an average SYNTAX score.However, such a protocol further increases the man-hours involved ingenerating a useable SYNTAX score. Finally, traditional SYNTAX scoringis usually performed based on a patient's entire coronary vasculature,without regard to any calculated functional result of an identifiedlesion. For example, a cardiologist may include in a SYNTAX scorepenalty points for a lesion that does not negatively impact downstreamFFR.

Thus, a need exists for systems and methods for assessing coronaryanatomy, coronary artery disease, myocardial perfusion, and coronaryartery flow noninvasively. In addition, a need exists for noninvasivelyevaluating the complexity and extent of disease in a patient's coronaryvasculature, and identifying the most appropriate intervention (e.g.,PCI vs. CABG). In addition, a need exists for evaluating the complexityand extent of disease in a patient's coronary vasculature byautomatically performing a numerical evaluation based on noninvasivelyobtained patient-specific data. A need also exists for systems andmethods that incorporate the functional impact of lesions, e.g. based onnoninvasive FFR calculation, when performing numerical evaluation of apatient's coronary vasculature.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

In accordance with an embodiment, a method is disclosed for providing acardiovascular score for a patient. The method includes receiving, usingat least one computer system, patient-specific data regarding a geometryof multiple coronary arteries of the patient; and creating, using atleast one computer system, a three-dimensional model representing atleast portions of the multiple coronary arteries based on thepatient-specific data. The method also includes evaluating, using atleast one computer system, multiple characteristics of at least some ofthe coronary arteries represented by the model; and generating, using atleast one computer system, the cardiovascular score based on theevaluation of the multiple characteristics.

In accordance with another embodiment, a computer-implemented method isdisclosed for generating a cardiovascular score for a patient. Themethod includes: receiving patient-specific data regarding a geometry ofmultiple coronary arteries of the patient, and creating a modelrepresenting at least portions of the multiple coronary arteries basedon the patient-specific data. The method further includes: evaluating aplurality of characteristics of at least portions of the coronaryarteries represented on the three-dimensional model; assigning numericalvalues to the plurality of characteristics based on the evaluation ofeach of the plurality of characteristics; and generating thecardiovascular score as a function of the assessed numerical values.

In accordance with another embodiment, a method is disclosed forproviding a cardiovascular score for a patient. The method includes:receiving, using at least one computer system, patient-specific dataregarding a geometry of multiple coronary arteries of the patient; andcreating, using at least one computer system, a three-dimensional modelrepresenting at least portions of the multiple coronary arteries basedon the patient-specific data. The method further includes: calculating,using at least one computer system, fractional flow reserve values at aplurality of locations of the at least portions of the coronaryarteries; evaluating, using at least one computer system, multiplecharacteristics of at least portions of the coronary arteriesrepresented by the model; and generating, using at least one computersystem, the cardiovascular score based on the evaluated multiplecharacteristics for portions of the coronary arteries having fractionalflow reserve values of at least a predetermined threshold value.

Additional embodiments and advantages will be set forth in part in thedescription which follows, and in part will be obvious from thedescription, or may be learned by practice of the disclosure. Theembodiments and advantages will be realized and attained by means of theelements and combinations particularly pointed out below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments and togetherwith the description, serve to explain the principles of the disclosure.

FIG. 1 shows an exemplary three-dimensional model of a patient'scoronary vasculature, generated using noninvasively obtained imagingdata;

FIG. 2 is a block diagram of an exemplary system and network for scoringpatient coronary vasculature, according to embodiments of the presentdisclosure;

FIG. 3 is a flow chart of an exemplary method for scoring patientcoronary vasculature, according to embodiments of the presentdisclosure;

FIG. 4 is a schematic diagram of a system for providing variousinformation relating to coronary blood flow in a specific patient,according to embodiments of the present disclosure; and

FIG. 5 is a block diagram of an exemplary method for providing variousinformation relating to coronary blood flow in a specific patient,according to embodiments of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings. Whereverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

In general, the present disclosure relates to performing a numericalevaluation of a patient's coronary vasculature (e.g., by scoring thecoronary arterial vasculature of the patient), based on analysis of apatient's anatomical coronary model and, optionally, various calculateddata relating to blood flow in a specific patient. In one embodiment,the disclosure relates to systems and methods for assessing coronaryanatomy, coronary artery disease, myocardial perfusion, and coronaryartery flow noninvasively. The presently disclosed systems and methodsassist cardiologists in evaluating the complexity and extent of diseasein a patient's coronary vasculature, and identifying the mostappropriate intervention (e.g., PCI vs. CABG). Accordingly, systems andmethods are disclosed for evaluating the complexity and extent ofdisease in a patient's coronary vasculature by automatically performinga numerical evaluation of noninvasively obtained patient-specific data.Moreover, systems and methods are disclosed for incorporating thefunctional impact of lesions, e.g. based on noninvasive FFR calculation,when performing numerical evaluation of a patient's coronaryvasculature. Such systems and methods may benefit cardiologists whodiagnose and plan treatments for patients with suspected coronary arterydisease.

In an exemplary embodiment, various information relating to blood flowin a specific patient (e.g., FFR values) is calculated using informationretrieved from the patient noninvasively. Various embodiments of systemsand methods for performing such calculations are described in greaterdetail in U.S. patent application Ser. No. 13/013,561, filed Jan. 25,2011, and entitled “Method and System for Patient-Specific Modeling ofBlood Flow,” which is assigned to the assignee of the presentapplication and which is hereby incorporated by reference in itsentirety.

In some embodiments, the information determined by the disclosed systemsand methods may relate to blood flow in the patient's coronaryvasculature. Alternatively, the determined information may relate toblood flow in other areas of the patient's vasculature, such as carotid,peripheral, abdominal, renal, and cerebral vasculature. The coronaryvasculature includes a complex network of vessels ranging from largearteries to arterioles, capillaries, venules, veins, etc. FIG. 1 depictsa model 220 of a portion of the coronary vasculature that circulatesblood to and within the heart and includes an aorta 2 that suppliesblood to a plurality of main coronary arteries 4 (e.g., the leftanterior descending (LAD) artery, the left circumflex (LCX) artery, theright coronary (RCA) artery, etc.), which may further divide intobranches of arteries or other types of vessels downstream from the aorta2 and the main coronary arteries 4. Thus, the exemplary systems andmethods may determine various information relating to blood flow withinthe aorta, the main coronary arteries, and/or other coronary arteries orvessels downstream from the main coronary arteries. Although the aortaand coronary arteries (and the branches that extend therefrom) arediscussed below, the disclosed systems and methods may also apply toother types of vessels.

In an exemplary embodiment, the information determined by the disclosedsystems and methods may include, but is not limited to, various bloodflow characteristics or parameters, such as blood flow velocity,pressure (or a ratio thereof), flow rate, and FFR at various locationsin the main coronary arteries, and/or other coronary arteries or vesselsdownstream from the main coronary arteries. This information may be usedto determine whether a lesion is functionally significant and/or whetherto treat the lesion. This information may be determined usinginformation obtained noninvasively from the patient. As a result, thedecision of whether and how to treat a lesion may be made without thecost and risk associated with invasive procedures.

FIG. 2 depicts a block diagram of an exemplary system and network forscoring a patient's coronary vasculature from vessel geometry,physiological information, and/or blood flow information. Specifically,FIG. 2 depicts a plurality of physicians 202 and third party providers204, any of whom may be connected to an electronic network 200, such asthe Internet, through one or more computers, servers, and/or handheldmobile devices. Physicians 202 and/or third party providers 204 maycreate or otherwise obtain images of one or more patients' cardiacand/or vascular systems, as will be described below with respect to FIG.3. The physicians 202 and/or third party providers 204 may also obtainany combination of patient-specific information, such as age, medicalhistory, blood pressure, blood viscosity, etc. Physicians 202 and/orthird party providers 204 may transmit the cardiac/vascular imagesand/or patient-specific information to computing system 206 over theelectronic network 200. Computing system 206 may include storage devicesfor storing images and data received from physicians 202 and/or thirdparty providers 204. Computing system 206 may also include processingdevices for processing images and data stored in the storage devices,according to methods disclosed herein.

FIG. 3 is a block diagram of an exemplary method 300 for numericallyevaluating, i.e., scoring, patient coronary vasculature from models ofvessel geometry, physiological information, and/or blood flowinformation, according to an exemplary embodiment of the presentdisclosure. More specifically, method 300 involves noninvasivelyevaluating the complexity and extent of disease in a patient's coronaryvasculature, and identifying the most appropriate intervention (e.g.,PCI vs. CABG). In one embodiment, method 300 incorporates the functionalimpact of lesions, e.g. based on noninvasive FFR calculation, whenperforming numerical evaluation of a patient's coronary vasculature. Inone embodiment, the method of FIG. 3 may be performed by computingsystem 206, based on information received from physicians 202 and/orthird party providers 204 over electronic network 200.

As shown in FIG. 3, method 300 may include receiving patient-specificanatomical data (step 302). For example, information regarding thepatient's anatomy (e.g., at least a portion of the aorta and a proximalportion of the main coronary arteries (and the branches extendingtherefrom) connected to the aorta), may be received and preprocessed.The patient-specific anatomical data may be obtained noninvasively,e.g., by CCTA, as will be described below.

In one embodiment, step 302 may include initially selecting a patient.For example, the patient may be selected by the physician when thephysician determines that information about the patient's coronary bloodflow is desired, e.g., if the patient is experiencing symptomsassociated with coronary artery disease, such as chest pain, heartattack, etc.

Patient-specific anatomical data may include data regarding the geometryof the patient's heart, e.g., at least a portion of the patient's aorta,a proximal portion of the main coronary arteries (and the branchesextending therefrom) connected to the aorta, and the myocardium. Thepatient-specific anatomical data may be obtained noninvasively, e.g.,using a noninvasive imaging method. For example, CCTA is an imagingmethod in which a user may operate a computer tomography (CT) scanner toview and create images of structures, e.g., the myocardium, the aorta,the main coronary arteries, and other blood vessels connected thereto.The CCTA data may be time-varying, e.g., to show changes in vessel shapeover a cardiac cycle. CCTA may be used to produce an image of thepatient's heart. For example, 64-slice CCTA data may be obtained, e.g.,data relating to slices of the patient's heart, and assembled into athree-dimensional image.

Alternatively, other noninvasive imaging methods, such as magneticresonance imaging (MRI) or ultrasound (US), or invasive imaging methods,such as digital subtraction angiography (DSA), may be used to produceimages of the structures of the patient's anatomy. The imaging methodsmay involve injecting the patient intravenously with a contrast agent toenable identification of the structures of the anatomy. The resultingimaging data (e.g., provided by CCTA, MRI, etc.) may be provided by athird-party vendor, such as a radiology lab or a cardiologist, by thepatient's physician, etc.

Other patient-specific anatomical data may also be determined from thepatient noninvasively. For example, physiological data such as thepatient's blood pressure, baseline heart rate, height, weight,hematocrit, stroke volume, etc., may be measured. The blood pressure maybe the blood pressure in the patient's brachial artery (e.g., using apressure cuff), such as the maximum (systolic) and minimum (diastolic)pressures.

The patient-specific anatomical data obtained as described above may betransferred over a secure communication line (e.g., via a network 200).For example, the data may be transferred to a server or other computersystem 206 for performing the computational analysis. In an exemplaryembodiment, the data may be transferred to a server or other computersystem operated by a service provider providing a web-based service.Alternatively, the data may be transferred to a computer system operatedby the patient's physician or other user. The transferred data may bereviewed to determine if the data is acceptable. The determination maybe performed by the user and/or by the computer system. For example, thetransferred data (e.g., the CCTA data and other data) may be verified bya user and/or by the computer system, e.g., to determine if the CCTAdata is complete (e.g., includes sufficient portions of the aorta andthe main coronary arteries) and corresponds to the correct patient.

The transferred data (e.g., the CCTA data and other data) may also bepreprocessed and assessed. The preprocessing and/or assessment may beperformed by a user and/or by the computer system and may include, e.g.,checking for misregistration, inconsistencies, or blurring in the CCTAdata, checking for stents shown in the CCTA data, checking for otherartifacts that may prevent the visibility of lumens of the bloodvessels, checking for sufficient contrast between the structures (e.g.,the aorta, the main coronary arteries, and other blood vessels) and theother portions of the patient, etc.

The transferred data may be evaluated to determine if the data isacceptable based on the verification, preprocessing, and/or assessmentdescribed above. During the verification, preprocessing, and/orassessment described above, the user and/or computer system may be ableto correct certain errors or problems with the data. If, however, thereare too many errors or problems, then the data may be determined to beunacceptable, and the user and/or computer system may generate arejection report explaining the errors or problems necessitating therejection of the transferred data. Optionally, a new CCTA scan may beperformed and/or the physiological data described above may be measuredfrom the patient again. If the transferred data is determined to beacceptable, then the method may proceed to step 304 for generation of athree-dimensional model, as described below.

As shown in FIG. 3, method 300 may then include generating athree-dimensional model of the patient's anatomy, based on the obtainedpatient-specific anatomical data (step 304). It will be appreciated thatas an alternative embodiment, a lower dimensional model may be used,such as a one-dimensional model of the vessels combined with analgorithm that encodes vessel sizes along their lengths.

According to one embodiment, the three-dimensional model of the coronaryvessels may be generated using the received CCTA data. FIG. 1 shows anexample of the surface of a three-dimensional model 220 generated usingthe CCTA data. For example, the model 220 may include, e.g., at least aportion of the aorta, at least a proximal portion of one or more maincoronary arteries connected to that portion of the aorta, at least aproximal portion of one or more branches connected to the main coronaryarteries, etc. The modeled portions of the aorta, the main coronaryarteries, and/or the branches may be interconnected and treelike suchthat no portion is disconnected from the rest of the model 220. Theprocess of generating the model 220 may be referred to as segmentation.

In one embodiment, computer system 206 may automatically segment atleast a portion of the aorta and the myocardium (or other heart tissue,or other tissue connected to the arteries to be modeled). The myocardiumor other tissue may also be segmented based on the CCTA data received instep 302. For example, the computer system may display to the user thethree-dimensional image or slices thereof produced from the CCTA data.The computer system may also segment at least a portion of the maincoronary arteries connected to the aorta. In an exemplary embodiment,the computer system may allow the user to select one or more coronaryartery root or starting points in order to segment the main coronaryarteries.

Segmentation may be performed using various methods. In one embodiment,segmentation is performed based on threshold-based segmentation,edge-based segmentation, graph theory, machine-learning methods, or acombination of those methods. Segmentation may be performedautomatically by the computer system based on user inputs or withoutuser inputs. For example, in an exemplary embodiment, the user mayprovide inputs to the computer system in order to generate a firstinitial model. The three-dimensional image may include portions ofvarying intensity of lightness. For example, lighter areas may indicatethe lumens of the aorta, the main coronary arteries, and/or thebranches. Darker areas may indicate the myocardium and other tissue ofthe patient's heart. The locations of the surfaces may be determinedbased on the contrast (e.g., relative darkness and lightness) and shapeof the myocardium compared to other structures of the heart in the CCTAdata. Thus, the geometry of the myocardium may be determined. Forexample, the CCTA data may be analyzed to determine the location of theinternal and external surfaces of the myocardium, e.g., the left and/orright ventricles.

The segmentation of the aorta, the myocardium, and/or the main coronaryarteries may be reviewed and/or corrected, if necessary. The reviewand/or correction may be performed by the computer system and/or theuser. For example, in an exemplary embodiment, the computer system mayautomatically review the segmentation, and the user may manually correctthe segmentation if there are any errors, e.g., if any portions of theaorta, the myocardium, and/or the main coronary arteries in the model220 are missing or inaccurate. Generation of the three-dimensional modelmay further involve identifying or generating one or more of thefollowing properties of the patient's anatomy based on thethree-dimensional model.

In one embodiment, the myocardial mass may be calculated, e.g., by thecomputer system analyzing model 220. For example, the myocardial volumemay be calculated based on the locations of the surfaces of themyocardium determined as described above, and the calculated myocardialvolume may be multiplied by the density of the myocardium to calculatethe myocardial mass. The density of the myocardium may be preset orcalculated.

In one embodiment, the centerlines of the various vessels (e.g., theaorta, the main coronary arteries, etc.) of the model 220 may also bedetermined, e.g., by the computer system. In an exemplary embodiment,the determination may be performed automatically by the computer systemanalyzing surfaces, shape, and contrast in the model 220. Thecenterlines may be reviewed and/or corrected, if necessary, either bythe computer system and/or the user. For example, in an exemplaryembodiment, the computer system may automatically review thecenterlines, and the user may manually correct the centerlines if thereare any errors, e.g., if any centerlines are missing or inaccurate.

In one embodiment, calcium or plaque (causing narrowing of a vessel) maybe detected, e.g., by the computer system. In an exemplary embodiment,the computer system may automatically detect the plaque by analyzingsurfaces and contrast in the model 220. For example, the plaque may bedetected in the three-dimensional image and excluded from the model 220.The plaque may be identified in the three-dimensional image since theplaque appears as areas that are even brighter or darker than the lumensof the aorta, the main coronary arteries, and/or the branches. Thus, theplaque may be detected by the computer system as having an intensityvalue above or below a set value or may be detected visually by theuser. Shape information, learning algorithms, and image analysis mayalso be used to detect and segment plaque. After detecting the plaque,the computer system may exclude the plaque from the model 220 so thatthe plaque is not considered as part of the lumen or open space in thevessels. Alternatively, the computer system may indicate the plaque onthe model 220 using a different color, shading, or other visualindicator than the aorta, the main coronary arteries, and/or thebranches.

In one embodiment, the detected plaque may be automatically segmented,e.g., by the computer system. For example, the plaque may be segmentedbased on the CCTA data. The CCTA data may be analyzed to locate theplaque (or a surface thereof) based on the contrast (e.g., relativedarkness and lightness) of the plaque compared to other structures ofthe heart in the CCTA data. Thus, the geometry of the plaque may also bedetermined. The segmentation of the plaque may be reviewed and/orcorrected, if necessary, either by the computer system and/or the user.For example, in an exemplary embodiment, the computer system mayautomatically review the segmentation, and the user may manually correctthe segmentation if there are any errors, e.g., if any plaque is missingor shown inaccurately.

In one embodiment, the branches connected to the main coronary arteriesmay be automatically segmented, e.g., by the computer system. Forexample, the branches may be segmented using similar methods forsegmenting the main coronary arteries. The computer system may alsoautomatically segment the plaque in the segmented branches.Alternatively, the branches (and any plaque contained therein) may besegmented at the same time as the main coronary arteries. Thesegmentation of the branches may be reviewed and/or corrected, ifnecessary, either by the computer system and/or the user. For example,in an exemplary embodiment, the computer system may automatically reviewthe segmentation, and the user may manually correct the segmentation ifthere are any errors, e.g., if any portions of the branches in the model220 are missing or inaccurate.

In one embodiment, the computer system may segment branches of model 220into one or more of the following defined segments, as defined athttp://www.syntaxscore.com, as referenced above:

-   1 RCA proximal From ostium to one half the distance to the acute    margin of the heart.-   2 RCA mid From end of first segment to acute margin of heart.-   3 RCA distal From the acute margin of the heart to the origin of the    posterior descending artery.-   4 Posterior descending Running in the posterior interventricular    groove.-   16 Posterolateral from RCA Posterolateral branch originating from    the distal coronary artery distal to the crux.-   16a Posterolateral from RCA First posterolateral branch from segment    16.-   16b Posterolateral from RCA Second posterolateral branch from    segment 16.-   16c Posterolateral from RCA Third posterolateral branch from segment    16.-   5 Left main From the ostium of the LCA through bifurcation into left    anterior descending and left circumflex branches.-   6 LAD proximal Proximal to and including first major septal branch.-   7 LAD mid LAD immediately distal to origin of first septal branch    and extending to the point where LAD forms an angle (RAO view). If    this angle is not identifiable this segment ends at one half the    distance from the first septal to the apex of the heart.-   8 LAD apical Terminal portion of LAD, beginning at the end of    previous segment and extending to or beyond the apex.-   9 First diagonal The first diagonal originating from segment 6 or 7.-   9a First diagonal a Additional first diagonal originating from    segment 6 or 7, before segment 8.-   10 Second diagonal Second diagonal originating from segment 8 or the    transition between segment 7 and 8.-   10a Second diagonal a Additional second diagonal originating from    segment 8.-   11 Proximal circumflex Main stem of circumflex from its origin of    left main to and including origin of first obtuse marginal branch.-   12 Intermediate/anterolateral Branch from trifurcating left main    other than proximal LAD or LCX. Belongs to the circumflex territory.-   12a Obtuse marginal a First side branch of circumflex running in    general to the area of obtuse margin of the heart.-   12b Obtuse marginal b Second additional branch of circumflex running    in the same direction as 12.-   13 Distal circumflex The stem of the circumflex distal to the origin    of the most distal obtuse marginal branch and running along the    posterior left atrioventricular grooves. Caliber may be small or    artery absent.-   14 Left posterolateral Running to the posterolateral surface of the    left ventricle. May be absent or a division of obtuse marginal    branch.-   14a Left posterolateral a Distal from 14 and running in the same    direction.-   14b Left posterolateral b Distal from 14 and 14 a and running in the    same direction.-   15 Posterior descending Most distal part of dominant left circumflex    when present. Gives origin to septal branches. When this artery is    present, segment 4 is usually absent.

The model 220 may be corrected if any misregistration, stents, or otherartifacts are located (e.g., during the review of the CCTA data), eitherby a user and/or by the computer system. For example, if amisregistration or other artifact (e.g., inconsistency, blurring, anartifact affecting lumen visibility, etc.) is located, the model 220 maybe reviewed and/or corrected to avoid an artificial or false change inthe cross-sectional area of a vessel (e.g., an artificial narrowing). Ifa stent is located, the model 220 may be reviewed and/or corrected toindicate the location of the stent and/or to correct the cross-sectionalarea of the vessel where the stent is located, e.g., based on the sizeof the stent.

If the segmentation of the model 220 is independently verified asacceptable, then, optionally, the model 220 may be output and smoothed.The smoothing may be performed by the user and/or by the computersystem. For example, ridges, points, or other discontinuous portions maybe smoothed. The model 220 may be output to a separate software moduleto be prepared for computational analysis, etc.

Once the three-dimensional model has been generated, method 300 mayfurther include evaluating characteristics of the patient's coronaryvasculature (step 306) based on analysis of the generated model. Asshown in FIG. 3, evaluated characteristics of the patient's coronaryvasculature may include one or more of the characteristics 350,including: (i) identification of left/right coronary dominance; (ii)identification of segments with threshold percentage of stenosis; (iii)identification of occlusions; (iv) identification ofbifurcations/trifurcations; (v) identification of ostial disease; (vi)computation of vessel tortuosity; (vii) computation of lengths ofdiseased segments; (viii) computation of dimensions of calcified plaque;(ix) identification of thrombus, and/or (x) identification of segmentswith diffuse disease. Notwithstanding the exemplary identifiedcharacteristics 350, evaluation step 306 may include evaluating anyother characteristic of patient coronary vasculature that is relevant toscoring an extent of cardiovascular complexity and/or disease.

In general, evaluation step 306 may include evaluating one or more ofcharacteristics 350, and assessing one or more numerical values or“points” based on the evaluation of each characteristic. For example, inone embodiment, evaluation step 306 may involve determining whether thepatient's left or right side of coronary vasculature is dominant. Forexample, computer system 206 may analyze model 220 to determine vesselsizes and topological structure, and process resulting data to identifythe existence of either left or right dominance, if any. In oneembodiment, computer system 206 may label the patient's posteriordescending artery (PDA), trace the PDA back to the patient's ostiumwhere it branches with either the left circumflex artery (LCX) (meaningthe coronaries are left dominant) or the right coronary artery (RCA)(meaning the coronaries are right dominant). The computer system 206 maydetect the PDA based on ventricular landmarks in model 220, and“vesselness response” (i.e., how similar a modeled shape appears to atypical vessel). Machine learning algorithms may also be used to comparethe patient anatomy to a database of other patients to establishleft/right dominance based on vessel sizes and position. In oneembodiment, the computer system identifies dominance based on distalvessel size, whereby if distal RCA is less than 1.5 mm, or some otherthreshold compared to the LCX, then the patient is left dominant.Conversely, proximal RCA size of a predetermined size may be indicativeof right hand dominance. The computer system 206 may then assign one ormore points based on any left or right dominance, and/or an extent ofany identified left or right dominance. Left and right dominance can beautomatically determined on the basis of connectivity analysis ofconstructed coronary topology. For example, if the PDA is connected withRCA, then the system may be classified as right-dominant, whereas if thePDA is connected with circumflex artery, then the system may beclassified as left-dominant.

The evaluation step 306 may also involve identifying segments withdisease. For example, computer system 206 may analyze model 220 todetermine which segments contain one or more lesions, e.g., based onidentification of segments with some predetermined threshold percentageof stenosis. Computer system 206 may identify lesions based on imagecontrast, or any other suitable technique. In one embodiment, computersystem 206 may identify segments as exhibiting stenosis if they have 50%or greater difference in contours compared to a vessel centerline.Stenotic lesions may be automatically identified by computing minimalocations of a cross-sectional area curve along a centerline withnumerical derivatives. The cross-sectional area curve may be smoothed(e.g., by Fourier smoothing) prior to performing a derivative operationin order to eliminate noise components in the area curve. The computersystem may identify diseased segments by projecting identified plaquesegmentation onto a vessel centerline. Machine learning may also beemployed to compare sections that were labeled with plaque to a databaseof expert annotated data to determine a confidence in rating thestenosis as > or <50%. Computer system 206 may then assign one or morepoints based on any diseased segments. In one embodiment, points may beassessed based on how many lesions a given segment contains, the type oflesions identified, and/or the location or identity of the segmentcontaining a lesion. For example, more points may be assigned to alesion in a critical vessel segment, as opposed to a less criticalvessel segment.

The evaluation step 306 may also involve identifying occlusions. Forexample, computer system 206 may analyze model 220 to identify segmentsor areas with no intra-luminal antegrade flow (TIMI 0) beyond the pointof occlusion. Computer system 206 may then assign one or more pointsbased on any identified occlusions. The algorithms may search for dropsin contrast down the vessel and detect where the lumen contrast isdiminished for a least a minimum distance. In one embodiment, points maybe assessed based on the location or identity of the segment containingan occlusion. For example, more points may be assigned to an occlusionin a critical vessel segment, as opposed to a less critical vesselsegment. Computer system 206 may also identify, and adjust assessedpoints based on, the type of occlusion (e.g., blunt stump, bridgingcollaterals, etc.) Occlusions may be automatically identified byanalyzing intensity (Hounsfield unit) changes along the centerline. Forexample, if the intensity value drops sharply below a certain threshold,then the position along the coronary can be classified as occlusions.The slope of intensity variation and magnitude of intensity may beutilized in this computation.

The evaluation step 306 may also include identifying any bifurcations ortrifurcations in the patient's vasculature. A bifurcation may beconsidered to be a division of a main, parent, branch into two daughterbranches of at least 1.5 mm. Bifurcation lesions may involve theproximal main vessel, the distal main vessel and the side branchaccording to the Medina classification. The smaller of the two daughterbranches should be designated as the ‘side branch’. In case of the mainstem, either the LCX or the LAD may be designated as the side branchdepending on their respective calibres. In one embodiment, bifurcationsmay be scored only for the following segment junctions (based on thenumbers assigned above to each coronary artery): 5/6/11, 6/7/9, 7/8/10,11/13/12a, 13/14/14a, 3/4/16 and 13/14/15. A trifurcation may beconsidered to be a division of a mainbranch into three branches of atleast 1.5 mm. In one embodiment, trifurcations may be scored only forthe following segment junctions: 3/4/16/16a, 5/6/11/12, 11/12a/12b/13,6/7/9/9a and 7/8/10/10a. Computer system 206 may then assess pointsbased on the identification of any bifurcation or trifurcation, and theidentity or location of any vessel segments containing such features. Inone embodiment, computer system 206 may assess points only for segmentnumbers of the bifurcation/trifurcation that have a diameter stenosis≧50% in direct contact with the bifurcation/trifurcation.Bifurcation/trifurcations may be identified automatically by countingthe number of intersections having a sphere (radius˜parent vesselradius) at junctions of constructed coronary centerlines. For example,if there are three intersections, then the junction is bifurcation,whereas if there are four intersections then the junction istrifurcation.

The evaluation step 306 may also involve identifying the existence ofostial disease. Specifically, computer system 206 may process image datafrom model 220 to identify stenosis that blends into the patient'saorta, or stenosis within some predetermined distance from the ostia,e.g., by using contours. Ostial disease can be automatically identifiedby evaluating vessel cross-sectional area or analyzing the existence ofplaques around the vicinity of ostia locations. Computer system 206 maythen assess points based on the identification of any ostial disease,and record the identity or location of any vessel segments containingsuch disease. Computer system 206 may also adjust the assessed pointsbased on the identified severity or location of any identified ostialdisease.

The evaluation step 306 may also involve identifying vessel segmentshaving severe tortuosity. For example, computer system 206 may, based onthe identified vessel centerlines discussed above, calculate thecurvature of the patient's coronary vessels. Computer system 206 mayassess one or more points to vessel segments having a predeterminedlevel of curvature, such as, for example, bends of 90 degrees or more,or three or more bends of 45-90 degrees proximal to the diseasedsegment. The number of bends can be automatically assessed by computingthe angle between tangential vectors of centerline in a sufficientlylarge interval size (e.g., three times or five times the diameter ofvessel). Computer system 206 may then assess points based on theidentification of any severe tortuosity, and record the identity orlocation of any vessel segments containing such severe tortuosity.Computer system 206 may also adjust the assessed points based on theidentified severity or location of any identified severe tortuosity.

The evaluation step 306 may also involve computing the lengths of anyidentified diseased segments. In one embodiment, computer system 206 mayanalyze model 220 to identify the length of any portion of stenosis thathas greater than or equal to 50% reduction in luminal diameter in theprojection where the lesion appears to be the longest. Computer system206 may then assess points based on the identified lengths of anydiseased segments, and record the identity or location of any vesselsegments containing stenosis of some predetermined threshold length.Computer system 206 may also adjust the assessed points based on thelength and/or location of any identified diseased segments.

The evaluation step 306 may also involve computing the dimensions of anyidentified calcified plaque. In one embodiment, computer system 206 mayanalyze model 220 to identify the volume and length of any calcifiedplaque within a stenosis region. Computer system 206 may alternativelyor additionally analyze model 220 to identify a surface area borderingidentified calcified plaque and adjacent lumen; and/or identify a ratioof such surface area within an identified stenosis region. Computersystem 206 may then assess points based on the dimensions of anyidentified calcified plaque, and record the locations or dimensions ofany identified calcified plaque. Computer system 206 may also adjust theassessed points based on the locations or dimensions of any identifiedcalcified plaque. The length of identified calcified plaque can beautomatically determined by projecting plaque segmentation ontocenterline. The effective cross-sectional area of plaque can be computedby dividing the plaque volume with computed length.

The evaluation step 306 may also involve identifying the existence ofthrombus. Specifically, computer system 206 may process image data frommodel 220 to identify thrombus, e.g., by using contours, contrast, etc.Computer system 206 may then assess points based on the identificationof any thrombus, and record the identity or location of any vesselsegments containing such thrombus. Computer system 206 may also adjustthe assessed points based on the identified severity or location of anyidentified thrombus. However, in one embodiment, thrombus may beweighted less than other disclosed characteristics 350, or omitted fromthe cardiovascular score altogether.

The evaluation step 306 may also involve identifying any diffusedisease. In one embodiment, computer system 206 may analyze model 220 tocalculate the mean vessel size from contours within or adjacent to anidentified stenosis segment. For example, computer system 206 mayidentify a stenosis segment as exhibiting diffuse disease if greaterthan 75 percent of a region is less than 2 mm in average diameter.Computer system 206 may then assess points based on the extent of anyidentified diffuse disease, and record the locations or dimensions ofany identified segments having diffuse disease. Computer system 206 mayalso adjust the assessed points based on the locations or dimensions ofany identified segments having diffuse disease.

Thus, evaluation step 306 may include identification and/or computationof one or more characteristics 350, and assessment of one or more pointsto a patient based on each identification, computation, extent, and/orlocation of those characteristics 350. Evaluation step 306 may then befollowed by generating a patient cardiovascular score based on one ormore of the evaluated characteristics and assessed points (step 308).For example, computer system 206 may generate a patient cardiovascularscore by executing an algorithm on the characteristics evaluated and/orpoints assessed in step 306. In one embodiment, the patientcardiovascular score may be generated by summing together all of thepoints assessed when evaluating the characteristics in step 306. Forinstance, computer system 206 may add together all of the pointsassessed for one or more of: (i) identification of left/right coronarydominance; (ii) identification of segments with threshold percentage ofstenosis; (iii) identification of occlusions; (iv) identification ofbifurcations/trifurcations; (v) identification of ostial disease; (vi)computation of tortuosity; (vii) computation of lengths of diseasedsegments; (viii) computation of dimensions of calcified plaque; (ix)identification of thrombus; and/or (x) identification of segments withdiffuse disease; so as to generate a single cardiovascular score.

In one embodiment, the single generated cardiovascular score may beweighted or normalized such that, if the generated score is less thansome predetermined score, then PCI may be the appropriate identifiedintervention, and if the score is greater than some predetermined score,then CABG may be the appropriate identified intervention. In oneembodiment, the generated cardiovascular score may be displayed to acardiologist or other user through a display device connected tocomputer system 206. Alternatively or additionally, the generatedcardiovascular score may be transmitted to a cardiologist or other thirdparty over the electronic network 200.

In one embodiment, the cardiovascular score generated in step 308 may beweighted or normalized to be compatible with the SYNTAX scoringtechnique described above. By way of example, the generatedcardiovascular score may weighted or normalized such that, if thegenerated score is less than 34, PCI may be the appropriate identifiedintervention, and if the score is greater than 34, then CABG may be theappropriate identified intervention.

In one embodiment, the patient cardiovascular score generated in step308 may be calculated based on a patient's entire coronary vasculature,without regard to each lesion or segment's functional significance onblood flow or pressure. Such a cardiovascular score may be referred toas an “anatomic cardiovascular score.” However, as described above, incertain circumstances, it may be advantageous to incorporate in thegenerated cardiovascular score only points relevant to a functionallysignificant lesion or characteristic 350. For example, if an area ofminor stenosis does not contribute to reduced blood flow or reduceddownstream blood pressure, then it may be desirable to avoid countingpoints associated with such an area into the generated cardiovascularscore. Such a cardiovascular score may be referred to as a “functionalcardiovascular score.”

Accordingly, in one embodiment, method 300 may also include calculatingFFR values at a plurality of locations of a patient's coronaryvasculature (step 312). Method 300 may also then include generating apatient cardiovascular score based on evaluated characteristics for asubset of locations having predetermined threshold level of FFR values(step 310), i.e., a “functional cardiovascular score.” For example, inone embodiment, at least one FFR value may be calculated for eachsegment of a patient's coronary vasculature. The patient cardiovascularscore may be calculated based only on points assessed to vessel segmentsthat have an FFR value below some predetermined threshold value. In analternative embodiment, functional values other than FFR values may beused to perform scoring only on functionally significant vesselsegments. For example, myocardial perfusion, flow rates, etc. may beconsidered and used to screen vessel segments whose characteristics maybe incorporated into the cardiovascular score. In one embodiment,instead of generating a cardiovascular score only based on segmentshaving a threshold functional significance, an anatomic (i.e., completevasculature) score may be modified by removing from the existingcalculation those segments not meeting the threshold functionalsignificance.

In one embodiment, the patient cardiovascular score may be calculatedbased only on points assessed to vessel segments that have an FFR valuebelow 0.9. In one embodiment, the patient cardiovascular score may becalculated based only on points assessed to vessel segments that have anFFR value below 0.8. In one embodiment, the patient cardiovascular scoremay be calculated based only on points assessed to vessel segments thathave an FFR value below 0.7. Thus, the patient cardiovascular score maybe calculated based only on vessel segments that actually contribute todysfunction of the patient's coronary vasculature. As described above,any of such patient cardiovascular scores may be referred to as a“functional cardiovascular score.”

It will be appreciated that FFR calculation step 312 may be performedaccording to any known methods, but preferably using noninvasivetechniques. For instance, in one embodiment, FFR calculation step 312may be performed according to the systems and methods for suchcalculations described in U.S. patent application Ser. No. 13/013,561,filed Jan. 25, 2011, and entitled “Method and System forPatient-Specific Modeling of Blood Flow,” which is assigned to theassignee of the present application and which is hereby incorporated byreference in its entirety. By way of example, FFR calculation step 312may be performed according to the system and method described below withrespect to FIGS. 4 and 5.

FIG. 4 shows aspects of a system for providing various information(e.g., FFR) relating to coronary blood flow in a specific patient,according to an exemplary embodiment. A three-dimensional model 10 ofthe patient's anatomy may be created using data obtained noninvasivelyfrom the patient as will be described below in more detail. Otherpatient-specific information may also be obtained noninvasively. In anexemplary embodiment, the portion of the patient's anatomy that isrepresented by the three-dimensional model 10 may include at least aportion of the aorta and a proximal portion of the main coronaryarteries (and the branches extending or emanating therefrom) connectedto the aorta.

Various physiological laws or relationships 20 relating to coronaryblood flow may be deduced, e.g., from experimental data as will bedescribed below in more detail. Using the three-dimensional anatomicalmodel 10 and the deduced physiological laws 20, a plurality of equations30 relating to coronary blood flow may be determined as will bedescribed below in more detail. For example, the equations 30 may bedetermined and solved using any numerical method, e.g., finitedifference, finite volume, spectral, lattice Boltzmann, particle-based,level set, finite element methods, etc. The equations 30 may be solvableto determine information (e.g., pressure, velocity, FFR, etc.) about thecoronary blood flow in the patient's anatomy at various points in theanatomy represented by the model 10.

The equations 30 may be solved using a computer 40. Based on the solvedequations, the computer 40 may output one or more images or simulationsindicating information relating to the blood flow in the patient'sanatomy represented by the model 10. For example, the image(s) mayinclude a simulated blood pressure model 50, a simulated blood flow orvelocity model 52, a computed FFR (cFFR) model 54, etc., as will bedescribed in further detail below. The simulated blood pressure model50, the simulated blood flow model 52, and the cFFR model 54 provideinformation regarding the respective pressure, velocity, and cFFR atvarious locations along three dimensions in the patient's anatomyrepresented by the model 10. cFFR may be calculated as the ratio of theblood pressure at a particular location in the model 10 divided by theblood pressure in the aorta, e.g., at the inflow boundary of the model10, under conditions of maximally increased coronary blood flow, e.g.,conventionally induced by intravenous administration of adenosine.

In an exemplary embodiment, the computer 40 may include one or morenon-transitory computer-readable storage devices that store instructionsthat, when executed by a processor, computer system, etc., may performany of the actions described herein for providing various informationrelating to blood flow in the patient. The computer 40 may include adesktop or portable computer, a workstation, a server, a personaldigital assistant, or any other computer system. The computer 40 mayinclude a processor, a read-only memory (ROM), a random access memory(RAM), an input/output (I/O) adapter for connecting peripheral devices(e.g., an input device, output device, storage device, etc.), a userinterface adapter for connecting input devices such as a keyboard, amouse, a touch screen, a voice input, and/or other devices, acommunications adapter for connecting the computer 40 to a network, adisplay adapter for connecting the computer 40 to a display, etc. Forexample, the display may be used to display the three-dimensional model10 and/or any images generated by solving the equations 30, such as thesimulated blood pressure model 50, the simulated blood flow model 52,and/or the cFFR model 54.

FIG. 5 shows aspects of a method for providing various informationrelating to blood flow in a specific patient, according to anotherexemplary embodiment. The method may include obtaining patient-specificanatomical data, such as information regarding the patient's anatomy(e.g., at least a portion of the aorta and a proximal portion of themain coronary arteries (and the branches extending therefrom) connectedto the aorta), and preprocessing the data (step 502). Thepatient-specific anatomical data may be obtained noninvasively, e.g., byCCTA.

A three-dimensional model of the patient's anatomy may be created basedon the obtained anatomical data (step 504). For example, thethree-dimensional model may be the three-dimensional model 10 (FIG. 4)or three-dimensional model 220 (FIG. 1) of the patient's anatomy,generated as discussed above with respect to FIG. 3.

The three-dimensional model may be prepared for analysis and boundaryconditions may be determined (step 506). For example, thethree-dimensional model 10 of the patient's anatomy described above inconnection with FIG. 4 may be trimmed and discretized into a volumetricmesh, e.g., a finite element or finite volume mesh. The volumetric meshmay be used to generate the equations 30 described above in connectionwith FIG. 4.

Boundary conditions may also be assigned and incorporated into theequations 30 described above in connection with FIG. 4. The boundaryconditions provide information about the three-dimensional model 10 atits boundaries, e.g., inflow boundaries, outflow boundaries, vessel wallboundaries, etc. The inflow boundaries may include the boundariesthrough which flow is directed into the anatomy of the three-dimensionalmodel, such as at an end of the aorta near the aortic root. Each inflowboundary may be assigned, e.g., with a prescribed value or field forvelocity, flow rate, pressure, or other characteristic, by coupling aheart model and/or a lumped parameter model to the boundary, etc. Theoutflow boundaries may include the boundaries through which flow isdirected outward from the anatomy of the three-dimensional model, suchas at an end of the aorta near the aortic arch, and the downstream endsof the main coronary arteries and the branches that extend therefrom.Each outflow boundary can be assigned, e.g., by coupling a lumpedparameter or distributed (e.g., a one-dimensional wave propagation)model. The prescribed values for the inflow and/or outflow boundaryconditions may be determined by noninvasively measuring physiologiccharacteristics of the patient, such as, but not limited to, cardiacoutput (the volume of blood flow from the heart), blood pressure,myocardial mass, etc. The vessel wall boundaries may include thephysical boundaries of the aorta, the main coronary arteries, and/orother coronary arteries or vessels of the three-dimensional model 10.

The computational analysis may be performed using the preparedthree-dimensional model and the determined boundary conditions (step508) to determine blood flow information for the patient. For example,the computational analysis may be performed with the equations 30 andusing the computer 40 described above in connection with FIG. 4 toproduce the images described above in connection with FIG. 4, such asthe simulated blood pressure model 50, the simulated blood flow model52, and/or the cFFR model 54.

The method may also include providing patient-specific treatment optionsusing the results (step 510). For example, the three-dimensional model10 created in step 504 and/or the boundary conditions assigned in step506 may be adjusted to model one or more treatments, e.g., placing acoronary stent in one of the coronary arteries represented in thethree-dimensional model 10 or other treatment options. Then, thecomputational analysis may be performed as described above in step 508in order to produce new images, such as updated versions of the bloodpressure model 50, the blood flow model 52, and/or the cFFR model 54.These new images may be used to determine a change in blood flowvelocity and pressure if the treatment option(s) are adopted.

The systems and methods disclosed herein may be incorporated into asoftware tool accessed by physicians to provide a noninvasive means toquantify blood flow in the coronary arteries and to assess thefunctional significance of coronary artery disease. In particular, thecalculated FFR values, as reflected in the cFFR model 54, may beincorporated into the step of generating a functional cardiovascularscore. Specifically, as discussed above with respect to step 310 ofmethod 300, a cardiovascular score may be generated based on evaluatedcharacteristics and assessed points for a subset of coronary segmentshaving FFR values with a predetermined functional range. Accordingly,the resulting cardiovascular score may be more relevant to actualfunction or dysfunction of various segments of a patient's coronaryvasculature, and more likely to be indicative of a desirableintervention technique (e.g., PCI vs. CABG).

In addition to calculating a functional cardiovascular score, physiciansmay use the system to predict the effect of medical, interventional,and/or surgical treatments on coronary artery blood flow. The system mayprevent, diagnose, manage, and/or treat disease in other portions of thecardiovascular system including arteries of the neck (e.g., carotidarteries), arteries in the head (e.g., cerebral arteries), arteries inthe thorax, arteries in the abdomen (e.g., the abdominal aorta and itsbranches), arteries in the arms, or arteries in the legs (e.g., thefemoral and popliteal arteries). The software tool may be interactive toenable physicians to develop optimal personalized therapies forpatients.

For example, the system may be incorporated at least partially into acomputer system, e.g., the computer 40 shown in FIG. 4 used by aphysician or other user. The computer system may receive data obtainednoninvasively from the patient (e.g., data used to create thethree-dimensional model 10, data used to apply boundary conditions orperform the computational analysis, etc.). For example, the data may beinput by the physician or may be received from another source capable ofaccessing and providing such data, such as a radiology or other medicallab. The data may be transmitted via a network or other system forcommunicating the data, or directly into the computer system. Thesoftware tool may use the data to produce and display thethree-dimensional model 10 or other models/meshes and/or any simulationsor other results determined by solving the equations 30 described abovein connection with FIG. 4, such as the simulated blood pressure model50, the simulated blood flow model 52, and/or the cFFR model 54. Thus,the system may perform steps 302-312 and/or steps 502-510. In step 502,the physician may provide further inputs to the computer system toselect possible treatment options, and the computer system may displayto the physician new simulations based on the selected possibletreatment options. Further, each of steps 302-312 and 502-510 shown inFIGS. 3 and 5 may be performed using separate computer systems, softwarepackages, or modules.

Alternatively, the system may be provided as part of a web-based serviceor other service, e.g., a service provided by an entity that is separatefrom the physician. The service provider may, for example, operate theweb-based service and may provide a web portal or other web-basedapplication (e.g., run on a server or other computer system operated bythe service provider) that is accessible to physicians or other usersvia a network or other methods of communicating data between computersystems. For example, the data obtained noninvasively from the patientmay be provided to the service provider, and the service provider mayuse the data to produce the three-dimensional model 10 or othermodels/meshes and/or any simulations or other results determined bysolving the equations 30 described above in connection with FIG. 4, suchas the simulated blood pressure model 50, the simulated blood flow model52, and/or the cFFR model 54. Then, the web-based service may transmitinformation relating to the three-dimensional model 10 or othermodels/meshes and/or the simulations so that the three-dimensional model10 and/or the simulations may be displayed to the physician on thephysician's computer system. Thus, the web-based service may performsteps 302-312 and/or steps 502-510 and any other steps described abovefor providing patient-specific information. In step 502, the physicianmay provide further inputs, e.g., to select possible treatment optionsor make other adjustments to the computational analysis, and the inputsmay be transmitted to the computer system operated by the serviceprovider (e.g., via the web portal). The web-based service may producenew simulations or other results based on the selected possibletreatment options, and may communicate information relating to the newsimulations back to the physician so that the new simulations may bedisplayed to the physician.

One or more of the steps described herein may be performed by one ormore human operators (e.g., a cardiologist or other physician, thepatient, an employee of the service provider providing the web-basedservice or other service provided by a third party, other user, etc.),or one or more computer systems used by such human operator(s), such asa desktop or portable computer, a workstation, a server, a personaldigital assistant, etc. The computer system(s) may be connected via anetwork or other method of communicating data.

Any aspect set forth in any embodiment may be used with any otherembodiment set forth herein. Every device and apparatus set forth hereinmay be used in any suitable medical procedure, may be advanced throughany suitable body lumen and body cavity, and may be used for imaging anysuitable body portion.

Various modifications and variations can be made in the disclosedsystems and processes without departing from the scope of thedisclosure. Other embodiments will be apparent to those skilled in theart from consideration of the specification and practice of thedisclosure disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the disclosure being indicated by the following claims.

What is claimed is:
 1. A method for automatically generating a cardiovascular score for a patient, the method comprising: receiving, using at least one computer system, non-invasively obtained patient-specific image data regarding a geometry of one or more coronary arteries of the patient; creating, using the at least one computer system, a three-dimensional model representing a plurality of portions of the one or more coronary arteries of the patient using the patient-specific image data; automatically determining, using the at least one computer system, a value of a blood flow property for each of the plurality of portions of the one or more coronary arteries using the three-dimensional model; automatically determining, using the at least one computer system, a subset of the portions of the one or more coronary arteries having values of the blood flow property meeting a predetermined threshold of functional significance; automatically evaluating, using the at least one computer system, multiple characteristics of the subset of the portions of the one or more coronary arteries, wherein at least one of the multiple characteristics is selected from the group of left/right dominance, a total occlusion, a presence of an ostial disease, a tortuosity value, a length of a diseased vessel segment, one or more dimensions of calcified plaque, a thrombus, and a presence of a diffuse disease, and wherein automatically evaluating the multiple characteristics comprises automatically generating a numerical measurement of at least part of each characteristic; automatically assigning, using the at least one computer system, a point value to each of the multiple characteristics based on the evaluation and generated numerical measurement of each characteristic; and for the subset of the portions of the one or more coronary arteries meeting the predetermined threshold of functional significance, automatically executing an algorithm using the assigned point values to generate a cardiovascular score.
 2. A method as in claim 1, wherein receiving the patient-specific data comprises receiving data generated from a noninvasive imaging modality.
 3. A method as in claim 1, wherein each of the multiple characteristics is selected from the group consisting of left/right dominance, identification of segment(s) of coronary arteries with disease, presence of total occlusions, presence of bifurcations and trifurcations, presence of ostial disease, computation of tortuosity, computation of length of diseased segment(s), computation of dimensions of calcified plaque, identification of thrombus, and presence of diffuse disease.
 4. A method as in claim 1, wherein the blood flow property includes one or more fractional flow reserve values for at least the subset of the coronary arteries.
 5. A method as in claim 4, wherein the predetermined threshold value is a fractional flow reserve value between 0.7 and 0.9.
 6. A method as in claim 1, wherein the method is performed without receiving any additional input, other than the patient-specific data regarding the geometry.
 7. A computer-implemented method for generating a cardiovascular score for a patient, the method comprising: receiving, using at least one computer system, non-invasively obtained patient-specific image data regarding a geometry of one or more coronary arteries of the patient; creating, using the at least one computer system, a three-dimensional model representing a plurality of portions of the one or more coronary arteries of the patient using the patient-specific image data; automatically determining, using the at least one computer system, a value of a blood flow property for each of a plurality of portions of the one or more coronary arteries using the three-dimensional model; automatically determining, using the at least one computer system and the three-dimensional model, a subset of the portions of the one or more coronary arteries having values of the blood flow property meeting a predetermined threshold of functional significance; for the subset of the portions of the one or more coronary arteries meeting the predetermined threshold of functional significance, automatically assigning numerical values to each of a plurality of characteristics of the subset of the portions of the one or more coronary arteries meeting the predetermined threshold of functional significance; and for the subset of the portions of the one or more coronary arteries meeting the predetermined threshold of functional significance, automatically executing an algorithm using the at least one computer system and the assigned numerical values to generate cardiovascular score.
 8. A method as in claim 7, wherein receiving the patient-specific data comprises receiving data generated from a noninvasive imaging modality.
 9. A method as in claim 7, wherein the characteristics are selected from the group consisting of left/right dominance, identification of segment(s) of coronary arteries with disease, presence of total occlusions, presence of bifurcations and trifurcations, presence of ostial disease, computation of tortuosity, computation of length of diseased segment(s), computation of dimensions of calcified plaque, identification of thrombus, and presence of diffuse disease.
 10. A method as in claim 7, wherein generating the cardiovascular score includes generating a fractional flow reserve value for at least one of the coronary arteries.
 11. A method as in claim 10, wherein the predetermined threshold value is a fractional flow reserve value between 0.7 and 0.9.
 12. A method as in claim 7, wherein the method is performed without receiving any additional input, other than the patient-specific data regarding the geometry.
 13. A method for providing a cardiovascular score for a patient, the method comprising: receiving, using at least one computer system, non-invasively obtained patient-specific image data regarding a geometry of one or more coronary arteries of the patient; creating, using the at least one computer system, a three-dimensional model representing one or more coronary arteries based on the non-invasively obtained patient-specific image data; automatically calculating, using the at least one computer system, fractional flow reserve values at a plurality of locations of the one or more coronary arteries using the three-dimensional model; automatically determining, using the at least one computer system a subset of the portions of the one or more coronary arteries having values of the fractional flow reserve meeting a predetermined threshold of functional significance; automatically evaluating, using the at least one computer system, multiple characteristics of the subset of the portions of the one or more coronary arteries represented by the three-dimensional model by identifying one or more geometrical features of the subset of portions extracted from the patient-specific image data; automatically assigning, using the at least one computer system, a numerical value to each of the multiple characteristics based on the evaluation; and automatically executing an algorithm, using the at least one computer system, to calculate a functional cardiovascular score for the determined subset of the portions of the one or more coronary arteries having a fractional flow reserve value meeting the predetermined threshold of functional significance.
 14. A method as in claim 13, wherein each of the multiple characteristics is selected from the group consisting of left/right dominance, identification of segment(s) of coronary arteries with disease, presence of total occlusions, presence of bifurcations and trifurcations, presence of ostial disease, computation of tortuosity, computation of length of diseased segment(s), computation of dimensions of calcified plaque, identification of thrombus, and presence of diffuse disease.
 15. A method as in claim 13, wherein the predetermined threshold value is one of 0.7, 0.8, and 0.9.
 16. A method as in claim 13, wherein the fractional flow reserve values are calculated based on the three-dimensional model and one of a reduced-order model, a physics-based model, and a lumped-parameter model of blood flow.
 17. A method as in claim 1, wherein the multiple characteristics are evaluated using the three-dimensional model.
 18. The method of claim 17, wherein the multiple characteristics are evaluated based on the computer system analyzing one or more of: image contrast, image contours, image gradients, post-processed images, image intensities, shape, and volumetric mesh of the three-dimensional model.
 19. A method as in claim 7, wherein the multiple characteristics meeting the predetermined threshold of functional significance are assigned numerical values using the three-dimensional model.
 20. The method of claim 19, wherein the multiple characteristics are assigned numerical values based on the computer system analyzing one or more of: image contrast, image contours, image gradients, post-processed images, image intensities, shape, and volumetric mesh of the three-dimensional model. 